CN115905454A - Data restoration method and device, electronic equipment and storage medium - Google Patents
Data restoration method and device, electronic equipment and storage medium Download PDFInfo
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- CN115905454A CN115905454A CN202211654589.8A CN202211654589A CN115905454A CN 115905454 A CN115905454 A CN 115905454A CN 202211654589 A CN202211654589 A CN 202211654589A CN 115905454 A CN115905454 A CN 115905454A
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Abstract
The invention provides a data recovery method, a data recovery device, electronic equipment and a storage medium. The data recovery method comprises the following steps: acquiring reference service data and service data to be repaired; analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result; inputting the semantic analysis result into a pre-trained rule generation engine to obtain a data repair rule; and repairing the service data to be repaired according to the data repairing rule. The data restoration method of the embodiment of the invention improves the reliability of data restoration.
Description
Technical Field
Embodiments of the present invention relate to the field of data processing technologies, and in particular, to a data recovery method and apparatus, an electronic device, and a storage medium.
Background
In the conventional data restoration, data rules are generated through natural language rules, and the rules of the natural language are converted into specific data rules through a rule engine, so that the data restoration is completed through the rules.
Data restoration in the prior art focuses on data restoration of metadata and natural language, and along with diversification change of data in an enterprise system, data restoration by combining natural language rules and metadata has certain limitation, so that data restoration cannot be performed on all diversified data. Therefore, the reliability of the data repair method is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data recovery method and apparatus, an electronic device, and a storage medium, so as to solve the above problems.
According to a first aspect of embodiments of the present invention, there is provided a data repair method, including: acquiring reference service data and service data to be repaired; analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result; inputting the semantic analysis result into a pre-trained rule generation engine to obtain a data restoration rule; and repairing the service data to be repaired according to the data repairing rule.
In another implementation manner of the present invention, acquiring reference service data includes: acquiring a service data table from a data management system; and filtering the data in the service data table through the data filtering model to obtain the reference service data.
In another implementation manner of the present invention, acquiring service data to be repaired includes: performing text characteristic analysis on the data in the service data table to obtain an analysis result; and determining the data to be repaired in the service data table according to the analysis result.
In another implementation manner of the present invention, determining data to be repaired in a service data table according to an analysis result includes: judging whether data with abnormal text attributes exist in the service data table according to the analysis result; and if the analysis result indicates that the data with the abnormal text attribute exists in the service data table, marking the data with the abnormal text attribute as the data to be repaired.
In another implementation manner of the present invention, repairing service data to be repaired according to a data repair rule includes: matching a repair rule corresponding to the data to be repaired from the data repair rules; and repairing the data to be repaired according to the repair rule regulations.
In another implementation manner of the present invention, the data recovery method further includes: performing text characteristic analysis on the repaired data to obtain an analysis result; if the analysis result is that the text attribute is abnormal, indicating that the repaired data is failed to be repaired, prompting an alarm for the repaired data, and repairing again; and if the text attribute is normal, indicating that the repaired data is successfully repaired.
In another implementation manner of the present invention, the data recovery method further includes: the data management system extracts user biological characteristic information through a deep learning model; judging whether the user biological characteristic information is matched with user identity information prestored in a data management system; if the biological characteristic information of the user is matched with the identity information of the user, the data operation authority is granted to the user, and if the biological characteristic information of the user is not matched with the identity information of the user, the data operation authority is refused to be granted to the user.
According to a second aspect of embodiments of the present invention, there is provided a data repair apparatus including: a data acquisition module: the method comprises the steps of acquiring reference service data and service data to be repaired; a data processing module: the semantic attribute analysis module is used for analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result; the semantic analysis result is input to a pre-trained rule generation engine to obtain a data restoration rule; a data recovery module: and the data recovery module is used for recovering the service data to be recovered according to the data recovery rule.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the data recovery method as described in any one of the above when executing the computer program.
According to a fourth aspect of embodiments of the present invention, there is provided a computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps in the data recovery method according to any one of the above.
In the data restoration method of the embodiment of the invention, semantic analysis results are obtained by performing semantic attribute analysis processing on a large amount of reference business data, the semantic analysis results generate data restoration rules through a pre-trained rule generation engine, and the data to be restored is restored through the data restoration rules. Because the semantic analysis result is obtained by performing semantic attribute analysis on a large amount of reference service data, the semantic analysis result contains text features of diversified data, so that the data restoration rule obtained through the semantic analysis result can restore the data of all diversified data, and the reliability of data restoration is improved.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present invention, the drawings used in the embodiments or prior art descriptions will be briefly introduced, and advantages and benefits of the solutions will become apparent to those skilled in the art by reading the detailed description of the embodiments below. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
In the drawings:
FIG. 1 is a flow chart of steps of a data repair method according to an embodiment of the present invention.
Fig. 2 is a block diagram of a data recovery apparatus according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to another embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described in detail below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments of the present invention shall fall within the scope of the protection of the embodiments of the present invention.
Fig. 1 is a flowchart of steps of a data recovery method according to an embodiment of the present invention, and as shown in fig. 1, this embodiment mainly includes the following steps:
s101, acquiring reference service data and service data to be repaired.
Illustratively, enterprise historical data is obtained, and required reference business data and business data to be repaired are extracted from the enterprise historical data, wherein the reference business data needs to meet data standards of corresponding business fields, and the data to be repaired is the historical data with the problems of data loss, data repetition, data inaccuracy and the like.
S102, analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result.
Illustratively, the semantic attributes of the benchmark business data are subjected to data analysis to obtain semantic analysis results, wherein the semantic analysis process can be realized by a semantic analysis algorithm which supports more than 130 languages including english, japanese, and russian in simplified and traditional languages.
S103, inputting the semantic analysis result into a pre-trained rule generation engine to obtain a data restoration rule.
Illustratively, a rule generating engine is trained in advance, an analysis result obtained by semantic analysis is input into the rule generating engine, and a data repairing rule is generated through the automatic data extraction, rotation, fusion and verification processes of the rule generating engine, wherein the data repairing rule comprises repairing rules corresponding to missing data repairing, error data repairing, data format repairing and the like.
And S104, repairing the service data to be repaired according to the data repairing rule.
Exemplarily, a data repair rule corresponding to the data problem type of the service data to be repaired is extracted from the data repair rule, and the data to be repaired is repaired according to the data repair rule.
In the data restoration method of the embodiment of the invention, semantic analysis results are obtained by performing semantic attribute analysis processing on a large amount of reference service data, the semantic analysis results generate data restoration rules through a pre-trained rule generation engine, and the data to be restored is restored through the data restoration rules. Because the semantic analysis result is obtained by performing semantic attribute analysis on a large amount of reference service data, the semantic analysis result contains text features of diversified data, so that the data restoration rule obtained through the semantic analysis result can restore the data of all diversified data, and the reliability of data restoration is improved.
In another implementation manner of the present invention, acquiring the reference service data includes: acquiring a service data table from a data management system; and filtering the data in the service data table through the data filtering model to obtain the reference service data.
Illustratively, a data management system of an enterprise is logged in, a required business data table is obtained from the data management system, and standard business data is obtained from the business data table through a pre-trained data filtering model to serve as reference business data.
In the method of the embodiment of the invention, the data in the service data table is filtered through the data filtering model, and the standard service data in the service data table is determined as the reference service data, so that the accuracy of the reference service data is improved.
In another implementation manner of the present invention, acquiring service data to be repaired includes: performing text characteristic analysis on the data in the service data table to obtain an analysis result; and determining the data to be repaired in the service data table according to the analysis result.
Illustratively, text feature analysis is performed on all data in the business data table to obtain an analysis result, data with an abnormal analysis result is searched from the business data table, the data with the abnormal analysis result is extracted to be used as data to be repaired, and the data to be repaired is historical data with the problems of data loss, data repetition, data inaccuracy and the like.
In the method of the embodiment of the invention, text characteristic analysis is carried out on the data in the business data table, the data with data deficiency, data repetition and inaccurate data are found out to be used as the data to be repaired, and the data to be repaired is repaired, so that the accuracy of the data in the business data table is ensured.
In another implementation manner of the present invention, determining data to be repaired in a service data table according to an analysis result includes: judging whether data with abnormal text attributes exist in the service data table according to the analysis result; and if the analysis result indicates that the data with the abnormal text attributes exist in the service data table, marking the data with the abnormal text attributes as the data to be repaired.
Exemplarily, judging whether data with abnormal text attributes exist in the service data table according to the text feature analysis result; and if the analysis result indicates that the data with the abnormal text attribute exists in the service data table, marking the data with the abnormal text attribute as the data to be repaired.
In the method of the embodiment of the invention, the data with the abnormal text attribute is used as the data to be repaired by judging whether the text attribute in the service data table is abnormal or not, and the corresponding repair rule can be searched through the abnormal text attribute during repair, so that the repaired data is more accurate.
In another implementation manner of the present invention, repairing service data to be repaired according to a data repair rule includes: matching a repair rule corresponding to the data to be repaired from the data repair rules; and repairing the data to be repaired according to the repair rule regulations.
Exemplarily, a data problem of data to be repaired is judged, a corresponding repair rule is matched from a data repair rule according to the data problem, the corresponding repair rule is used, and the data to be repaired is repaired, wherein the data repair rule comprises corresponding repair rules for repairing missing data, repairing error data, repairing data formats and the like.
In the method of the embodiment of the invention, the data to be repaired is repaired by matching the repair rule rules corresponding to the data problems of the data to be repaired, and the accuracy of the repair result can be ensured and the reliability of data repair is improved because the used repair rule rules have pertinence.
In another implementation manner of the present invention, the data recovery method further includes: performing text characteristic analysis on the repaired data to obtain an analysis result; if the analysis result is that the text attribute is abnormal, indicating that the repaired data is failed to be repaired, prompting an alarm for the repaired data, and repairing again; and if the text attribute is normal, indicating that the repaired data is successfully repaired.
Illustratively, text characteristic analysis is performed again on the repaired data, whether the repair is successful or not is judged according to the analysis result, if the text characteristic analysis result still indicates that the text attribute is abnormal, the data repair is failed, an alarm is prompted to the data, the repair is performed again, if the text attribute is normal, the repaired data is indicated to be successfully repaired, and the successfully repaired data is stored in the service data table.
In the method of the embodiment of the invention, the text attribute analysis is carried out on the repaired data again to judge whether the data is successfully repaired or not, and the data is repaired again when the repair fails, so that the condition that the data which fails to be repaired is stored in the service data table is avoided, the accuracy of the repaired data is ensured, and the reliability of the data repairing method is further improved.
In another implementation manner of the present invention, the data recovery method further includes: the data management system extracts user biological characteristic information through a deep learning model; judging whether the user biological characteristic information is matched with user identity information prestored in a data management system; if the biological characteristic information of the user is matched with the identity information of the user, the data operation authority is granted to the user, and if the biological characteristic information of the user is not matched with the identity information of the user, the data operation authority is refused to be granted to the user.
Illustratively, a data administrator registers an account in a data management system through authentication identity information, identifies a person by face after the account is successfully registered, grants data operation authority to the administrator if a face identification result is matched with the authenticated identity information, and does not grant data operation authority to the administrator if the face identification result is not matched with the authenticated identity information, wherein the data operation authority comprises the authority of downloading, modifying, deleting and the like of a business data table.
In the scheme of the embodiment of the invention, whether the data operation authority is granted or not is determined by judging whether the biological characteristic information of the user is matched with the authenticated identity information or not, so that the business data is prevented from being tampered by others, the safety of the business data of an enterprise is ensured, and meanwhile, the characteristics are automatically extracted by utilizing the deep learning model, so that the blindness and the difference in the process of manually designing the characteristics can be avoided.
In another implementation manner of the present invention, the data recovery method further includes: acquiring a data standard of service data; and generating a business data rule according to the data standard and the data repair rule of the business data.
Illustratively, a data standard of the service data and a data repair rule are fused to generate a service data rule, and the service data rule can specifically repair the service data to be repaired, which has a complex data standard, according to the service data standard.
In the scheme of the embodiment of the invention, the service data to be repaired with more complex data standards is repaired in a targeted way by combining the service data standards and the data repair rules, so that the accuracy of the service data with more complex data standards is ensured, and the data quality of the service data is further improved.
In another implementation manner of the present invention, the data recovery method further includes: and establishing a data label for the successfully repaired data, and storing the successfully repaired data into a corresponding service data table.
Illustratively, a data tag is established for successfully repaired data, where the data tag may include problems existing in the data, data repair regulations used, and the like, so that a user can conveniently check the successfully repaired data, store the successfully repaired data in a corresponding service data table, and update data to be repaired in the service data table one by one after the repair is completed.
In the scheme of the embodiment of the invention, the data label is established for the repaired data, so that the user can conveniently check the repaired data subsequently, the successfully repaired data is stored into the corresponding service data table, the data to be repaired in the service data table is updated, and the accuracy of the service data in the service data table is ensured.
In another implementation manner of the present invention, the data type of the service data to be repaired is determined, and if the data type is confidential service data and the analysis result of the service data to be repaired is a value error, the service data to be repaired is prompted to alarm and alarm information is sent to a manager of the data management system.
Illustratively, confidential business data including employees of a company, customer data, organization information, supplier information, etc. are found by data types, which are authoritative and global, belong to the enterprise assets of the company, and are not modifiable at will. If the data type is confidential service data and the analysis result of the service data to be repaired is that the numerical value is wrong, the service data to be repaired cannot be randomly repaired by using the data repair rule, at the moment, the service data to be repaired is prompted to be alarmed, the alarm information is sent to a manager of the data management system, and the manager can check the data. And if the analysis result of the service data to be repaired is the problem that the data format and the like are irrelevant to the numerical value, repairing the service data to be repaired by using the data repairing rule.
In the scheme of the embodiment of the invention, the confidential business data is judged and searched through the data type, then the data problem of the confidential business data is analyzed, the data to be repaired with the numerical value problem is searched, and the part of data is handed to a manager for being checked, so that the problem that other people falsify the confidential business data is avoided, and the safety and the accuracy of the confidential business data are ensured.
In the data restoration method of the embodiment of the invention, semantic analysis results are obtained by performing semantic attribute analysis processing on a large amount of reference service data, the semantic analysis results generate data restoration rules through a pre-trained rule generation engine, and the data to be restored is restored through the data restoration rules. Because the semantic analysis result is obtained by performing semantic attribute analysis on a large amount of reference service data, the semantic analysis result contains text features of diversified data, so that the data repair rule obtained through the semantic analysis result can perform data repair on all diversified data, and the reliability of data repair is improved.
Fig. 2 is a block diagram of a data recovery apparatus 200 according to an embodiment of the present invention, as shown in fig. 2, the embodiment mainly includes:
the data acquisition module 201: the method is used for acquiring the reference service data and the service data to be repaired.
Illustratively, enterprise historical data is obtained, and required reference business data and business data to be repaired are extracted from the enterprise historical data, wherein the reference business data needs to meet data standards of corresponding business fields, and the data to be repaired is the historical data with the problems of data loss, data repetition, data inaccuracy and the like.
The data processing module 202: the system is used for analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result; and the semantic analysis result is input to a pre-trained rule generation engine to obtain the data restoration rule.
Exemplarily, performing data analysis on semantic attributes of the reference business data to obtain a semantic analysis result, wherein the semantic analysis process may be implemented by a semantic analysis algorithm supporting 130 languages including english, japanese and russian in simple 5. Pre-training a rule generating engine, inputting an analysis result obtained by semantic analysis into the rule generating engine, and automatically executing the processes of data extraction, rotation, fusion and verification through the rule generating engine to generate a data repairing rule, wherein the data repairing rule comprises repairing rules corresponding to missing data repairing, error data repairing, data format repairing and the like.
The data repair module 203: and the data recovery module is used for performing 0 recovery on the service data to be recovered according to the data recovery rule.
Illustratively, a data repair rule corresponding to the data problem type of the service data to be repaired is extracted from the data repair rule, and the data to be repaired is repaired through the data repair rule.
In the data restoration device 200 according to the embodiment of the present invention, a semantic analysis result is obtained by performing semantic attribute analysis processing on a large amount of reference service data, the semantic analysis result generates a data restoration rule by using a rule 5 generation engine trained in advance, and data to be restored is restored by using the data restoration rule. Because the semantic analysis result is obtained by performing semantic attribute analysis on a large amount of reference service data, the semantic analysis result contains text features of diversified data, so that the data restoration rule obtained through the semantic analysis result can restore the data of all diversified data, and the reliability of data restoration is improved.
In another implementation manner of the present invention, the data obtaining module 201 is further configured to obtain a service data table from the data management system 0; the data in the business data table is filtered through a data filtering model,
and obtaining the reference service data.
In another implementation manner of the present invention, the data obtaining module 201 is further configured to perform text feature analysis on the data in the service data table to obtain an analysis result; and determining the data to be repaired in the service data table according to the analysis result.
5 in another implementation manner of the present invention, the data processing module 202 is further configured to determine whether data with abnormal text attributes exists in the service data table according to the analysis result; and if the analysis result indicates that the data with the abnormal text attribute exists in the service data table, marking the data with the abnormal text attribute as the data to be repaired.
In another implementation manner of the present invention, the data processing module 202 is further configured to match a repair rule corresponding to the data to be repaired from the data repair rule; and repairing the data to be repaired according to the repair rule regulations.
In another implementation manner of the present invention, the data processing module 202 is further configured to perform text feature analysis on the repaired data to obtain an analysis result; if the analysis result is that the text attribute is abnormal, indicating that the repaired data is failed to be repaired, prompting an alarm for the repaired data, and repairing again; and if the analysis result is that the text attribute is normal, indicating that the repaired data is successfully repaired.
In another implementation manner of the present invention, the data obtaining module 201 is further configured to extract, by the data management system, the user biometric information through a deep learning model; judging whether the user biological characteristic information is matched with user identity information prestored in a data management system; if the biological characteristic information of the user is matched with the identity information of the user, the data operation authority is granted to the user, and if the biological characteristic information of the user is not matched with the identity information of the user, the data operation authority is refused to be granted to the user.
The apparatus of this embodiment is used to implement the corresponding method in the foregoing method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again. In addition, the functional implementation of each module in the apparatus of this embodiment can refer to the description of the corresponding part in the foregoing method embodiment, and is not described herein again.
As shown in fig. 3, the electronic device 300 may include: a processor (processor) 301, a memory (memory) 303, a communication bus 304, and a communication Interface (Communications Interface) 305.
Wherein:
the processor 301, the memory 303 and the communication interface 305 communicate with each other via a communication bus 304.
A communication interface 305 for communicating with other electronic devices or servers.
The processor 301 is configured to execute the program 302, and may specifically execute the steps of any data recovery method in the foregoing embodiments.
In particular, program 302 may include program code comprising computer operating instructions.
The processor 301 may be a central processing unit CPU, or an Application Specific Integrated Circuit ASIC (Application Specific Integrated Circuit), or one or more Integrated circuits configured to implement embodiments of the present Application. The intelligent device comprises one or more processors which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 303 for storing the program 302. Memory 303 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 302 may be specifically adapted to cause the processor 301 to execute steps to implement any of the data repair methods described in the embodiments. For specific implementation of each step in the program 302, reference may be made to corresponding descriptions in steps and units executed by any data recovery method in the above steps, which are not described herein again. It is clear to those skilled in the art that for convenience and brevity of description, the specific working procedures of the above described apparatus and modules may be described with reference to the corresponding procedures in the foregoing method embodiments.
The exemplary embodiments of this application also provide a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the methods of the embodiments of this application.
The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA. It will be appreciated that a computer, processor, microprocessor controller, or programmable hardware includes memory components (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by a computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code for implementing the methods illustrated herein, execution of the code transforms the general-purpose computer into a special-purpose computer for performing the methods illustrated herein.
So far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
It should be noted that all directional indicators (such as up, down, left, right, back \8230;) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the motion situation, etc. in a specific posture (as shown in the attached drawings), and if the specific posture is changed, the directional indicator is changed accordingly.
In the description of the present invention, the terms "first" and "second" are used merely for convenience in describing different components or names, and are not to be construed as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
It should be noted that, although the specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, the present invention should not be construed as limited to the scope of the present invention. Various modifications and changes may be made by those skilled in the art without inventive step within the scope of the present invention as described in the appended claims.
The examples of the embodiments of the present invention are intended to briefly describe the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and the embodiments of the present invention are not unduly limited.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of data repair, comprising:
acquiring reference service data and service data to be repaired;
analyzing and processing the semantic attributes of the reference service data to obtain a semantic analysis result;
inputting the semantic analysis result into a pre-trained rule generation engine to obtain a data restoration rule;
and repairing the service data to be repaired according to the data repairing rule.
2. The method of claim 1, wherein the obtaining the reference traffic data comprises:
acquiring a service data table from a data management system;
and filtering the data in the service data table through a data filtering model to obtain the reference service data.
3. The method of claim 2, wherein the obtaining the service data to be repaired comprises:
performing text characteristic analysis on the data in the service data table to obtain an analysis result;
and determining the data to be repaired in the service data table according to the analysis result.
4. The method of claim 3, wherein the determining the data to be repaired in the service data table according to the analysis result comprises:
judging whether data with abnormal text attributes exist in the business data table according to the analysis result;
and if the analysis result indicates that the data with the abnormal text attribute exists in the service data table, marking the data with the abnormal text attribute as the data to be repaired.
5. The method according to claim 1, wherein the repairing the service data to be repaired according to the data repair rule includes:
matching a repair rule corresponding to the data to be repaired from the data repair rules;
and repairing the data to be repaired according to the repairing rule regulations.
6. The method of claim 5, further comprising:
performing text feature analysis on the repaired data to obtain an analysis result;
if the analysis result is that the text attribute is abnormal, indicating that the repaired data is failed to be repaired, prompting an alarm for the repaired data, and performing repairing again;
and if the analysis result is that the text attribute is normal, indicating that the repaired data is successfully repaired.
7. The method of claim 2, further comprising:
the data management system extracts user biological characteristic information through a deep learning model;
judging whether the user biological characteristic information is matched with user identity information prestored in the data management system;
if the user biological characteristic information is matched with the user identity information, data operation permission is granted to the user, and if the user biological characteristic information is not matched with the user identity information, the data operation permission is refused to be granted to the user.
8. A data repair device, comprising:
a data acquisition module: the method comprises the steps of acquiring reference service data and service data to be repaired;
a data processing module: the semantic attribute analysis module is used for analyzing and processing the semantic attributes of the reference business data to obtain a semantic analysis result; the semantic analysis result is input to a pre-trained rule generation engine to obtain a data restoration rule;
a data recovery module: and the data recovery module is used for recovering the service data to be recovered according to the data recovery rule.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the steps of the data repair method according to any one of claims 1 to 7 when executing the computer program.
10. A computer storage medium, characterized in that the computer storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps in the data repair method according to any one of claims 1 to 7.
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| CN202211654589.8A CN115905454A (en) | 2022-12-22 | 2022-12-22 | Data restoration method and device, electronic equipment and storage medium |
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